






'''simple docstring'''







import tempfile

import torch

from diffusers import (
    DEISMultistepScheduler,
    DPMSolverMultistepScheduler,
    DPMSolverSinglestepScheduler,
    UniPCMultistepScheduler,
)

from .test_schedulers import SchedulerCommonTest




class 		UpperCamelCase__     (a      ):




		'''simple docstring'''





		_UpperCamelCase						=       (DEISMultistepScheduler,)
		_UpperCamelCase						=       (('num_inference_steps', 25),)






		def       UpperCamelCase_       (   self ,**_lowerCAmelCase							):
								lowerCamelCase__								=	{
								    """num_train_timesteps""": 10_00,
								    """beta_start""": 0.0001,
								    """beta_end""": 0.02,
								    """beta_schedule""": """linear""",
								    """solver_order""": 2,
								}

								config.update(**_lowerCAmelCase							)
								return config






		def       UpperCamelCase_       (   self ,_lowerCAmelCase=0 ,**_lowerCAmelCase							):
								lowerCamelCase__								=	dict(self.forward_default_kwargs							)
								lowerCamelCase__								=	kwargs.pop("""num_inference_steps""" ,_lowerCAmelCase							)
								lowerCamelCase__								=	self.dummy_sample
								lowerCamelCase__								=	0.1 * sample
								lowerCamelCase__								=	[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
														lowerCamelCase__								=	self.get_scheduler_config(**_lowerCAmelCase							)
														lowerCamelCase__								=	scheduler_class(**_lowerCAmelCase							)
														scheduler.set_timesteps(_lowerCAmelCase							)
														# copy over dummy past residuals
														lowerCamelCase__								=	dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(_lowerCAmelCase							)
																				lowerCamelCase__								=	scheduler_class.from_pretrained(_lowerCAmelCase							)
																				new_scheduler.set_timesteps(_lowerCAmelCase							)
																				# copy over dummy past residuals
																				lowerCamelCase__								=	dummy_past_residuals[: new_scheduler.config.solver_order]

														lowerCamelCase__							,      lowerCamelCase__								=	sample, sample
														for t in range(_lowerCAmelCase ,time_step + scheduler.config.solver_order + 1							):
																				lowerCamelCase__								=	scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase							).prev_sample
																				lowerCamelCase__								=	new_scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase							).prev_sample

																				assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"






		def       UpperCamelCase_       (   self							):
								pass






		def       UpperCamelCase_       (   self ,_lowerCAmelCase=0 ,**_lowerCAmelCase							):
								lowerCamelCase__								=	dict(self.forward_default_kwargs							)
								lowerCamelCase__								=	kwargs.pop("""num_inference_steps""" ,_lowerCAmelCase							)
								lowerCamelCase__								=	self.dummy_sample
								lowerCamelCase__								=	0.1 * sample
								lowerCamelCase__								=	[residual + 0.2, residual + 0.15, residual + 0.10]

								for scheduler_class in self.scheduler_classes:
														lowerCamelCase__								=	self.get_scheduler_config()
														lowerCamelCase__								=	scheduler_class(**_lowerCAmelCase							)
														scheduler.set_timesteps(_lowerCAmelCase							)

														# copy over dummy past residuals (must be after setting timesteps)
														lowerCamelCase__								=	dummy_past_residuals[: scheduler.config.solver_order]

														with tempfile.TemporaryDirectory() as tmpdirname:
																				scheduler.save_config(_lowerCAmelCase							)
																				lowerCamelCase__								=	scheduler_class.from_pretrained(_lowerCAmelCase							)
																				# copy over dummy past residuals
																				new_scheduler.set_timesteps(_lowerCAmelCase							)

																				# copy over dummy past residual (must be after setting timesteps)
																				lowerCamelCase__								=	dummy_past_residuals[: new_scheduler.config.solver_order]

														lowerCamelCase__								=	scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase							).prev_sample
														lowerCamelCase__								=	new_scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase							).prev_sample

														assert torch.sum(torch.abs(output - new_output							)							) < 1E-5, "Scheduler outputs are not identical"






		def       UpperCamelCase_       (   self ,_lowerCAmelCase=None ,**_lowerCAmelCase							):
								if scheduler is None:
														lowerCamelCase__								=	self.scheduler_classes[0]
														lowerCamelCase__								=	self.get_scheduler_config(**_lowerCAmelCase							)
														lowerCamelCase__								=	scheduler_class(**_lowerCAmelCase							)

								lowerCamelCase__								=	self.scheduler_classes[0]
								lowerCamelCase__								=	self.get_scheduler_config(**_lowerCAmelCase							)
								lowerCamelCase__								=	scheduler_class(**_lowerCAmelCase							)

								lowerCamelCase__								=	10
								lowerCamelCase__								=	self.dummy_model()
								lowerCamelCase__								=	self.dummy_sample_deter
								scheduler.set_timesteps(_lowerCAmelCase							)

								for i, t in enumerate(scheduler.timesteps							):
														lowerCamelCase__								=	model(_lowerCAmelCase ,_lowerCAmelCase							)
														lowerCamelCase__								=	scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							).prev_sample

								return sample






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	dict(self.forward_default_kwargs							)

								lowerCamelCase__								=	kwargs.pop("""num_inference_steps""" ,_lowerCAmelCase							)

								for scheduler_class in self.scheduler_classes:
														lowerCamelCase__								=	self.get_scheduler_config()
														lowerCamelCase__								=	scheduler_class(**_lowerCAmelCase							)

														lowerCamelCase__								=	self.dummy_sample
														lowerCamelCase__								=	0.1 * sample

														if num_inference_steps is not None and hasattr(_lowerCAmelCase ,"""set_timesteps"""							):
																				scheduler.set_timesteps(_lowerCAmelCase							)
														elif num_inference_steps is not None and not hasattr(_lowerCAmelCase ,"""set_timesteps"""							):
																				lowerCamelCase__								=	num_inference_steps

														# copy over dummy past residuals (must be done after set_timesteps)
														lowerCamelCase__								=	[residual + 0.2, residual + 0.15, residual + 0.10]
														lowerCamelCase__								=	dummy_past_residuals[: scheduler.config.solver_order]

														lowerCamelCase__								=	scheduler.timesteps[5]
														lowerCamelCase__								=	scheduler.timesteps[6]

														lowerCamelCase__								=	scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase							).prev_sample
														lowerCamelCase__								=	scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase ,**_lowerCAmelCase							).prev_sample

														self.assertEqual(output_a.shape ,sample.shape							)
														self.assertEqual(output_a.shape ,output_a.shape							)






		def       UpperCamelCase_       (   self							):
								# make sure that iterating over schedulers with same config names gives same results
								# for defaults
								lowerCamelCase__								=	DEISMultistepScheduler(**self.get_scheduler_config()							)
								lowerCamelCase__								=	self.full_loop(scheduler=_lowerCAmelCase							)
								lowerCamelCase__								=	torch.mean(torch.abs(_lowerCAmelCase							)							)

								assert abs(result_mean.item() - 0.2_3916							) < 1E-3

								lowerCamelCase__								=	DPMSolverSinglestepScheduler.from_config(scheduler.config							)
								lowerCamelCase__								=	DPMSolverMultistepScheduler.from_config(scheduler.config							)
								lowerCamelCase__								=	UniPCMultistepScheduler.from_config(scheduler.config							)
								lowerCamelCase__								=	DEISMultistepScheduler.from_config(scheduler.config							)

								lowerCamelCase__								=	self.full_loop(scheduler=_lowerCAmelCase							)
								lowerCamelCase__								=	torch.mean(torch.abs(_lowerCAmelCase							)							)

								assert abs(result_mean.item() - 0.2_3916							) < 1E-3






		def       UpperCamelCase_       (   self							):
								for timesteps in [25, 50, 1_00, 9_99, 10_00]:
														self.check_over_configs(num_train_timesteps=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								self.check_over_configs(thresholding=_lowerCAmelCase							)
								for order in [1, 2, 3]:
														for solver_type in ["logrho"]:
																				for threshold in [0.5, 1.0, 2.0]:
																										for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    thresholding=_lowerCAmelCase ,prediction_type=_lowerCAmelCase ,sample_max_value=_lowerCAmelCase ,algorithm_type="""deis""" ,solver_order=_lowerCAmelCase ,solver_type=_lowerCAmelCase ,)






		def       UpperCamelCase_       (   self							):
								for prediction_type in ["epsilon", "v_prediction"]:
														self.check_over_configs(prediction_type=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								for algorithm_type in ["deis"]:
														for solver_type in ["logrho"]:
																				for order in [1, 2, 3]:
																										for prediction_type in ["epsilon", "sample"]:
																																self.check_over_configs(
																																    solver_order=_lowerCAmelCase ,solver_type=_lowerCAmelCase ,prediction_type=_lowerCAmelCase ,algorithm_type=_lowerCAmelCase ,)
																																lowerCamelCase__								=	self.full_loop(
																																    solver_order=_lowerCAmelCase ,solver_type=_lowerCAmelCase ,prediction_type=_lowerCAmelCase ,algorithm_type=_lowerCAmelCase ,)
																																assert not torch.isnan(_lowerCAmelCase							).any(), "Samples have nan numbers"






		def       UpperCamelCase_       (   self							):
								self.check_over_configs(lower_order_final=_lowerCAmelCase							)
								self.check_over_configs(lower_order_final=_lowerCAmelCase							)






		def       UpperCamelCase_       (   self							):
								for num_inference_steps in [1, 2, 3, 5, 10, 50, 1_00, 9_99, 10_00]:
														self.check_over_forward(num_inference_steps=_lowerCAmelCase ,time_step=0							)






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.full_loop()
								lowerCamelCase__								=	torch.mean(torch.abs(_lowerCAmelCase							)							)

								assert abs(result_mean.item() - 0.2_3916							) < 1E-3






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.full_loop(prediction_type="""v_prediction"""							)
								lowerCamelCase__								=	torch.mean(torch.abs(_lowerCAmelCase							)							)

								assert abs(result_mean.item() - 0.091							) < 1E-3






		def       UpperCamelCase_       (   self							):
								lowerCamelCase__								=	self.scheduler_classes[0]
								lowerCamelCase__								=	self.get_scheduler_config(thresholding=_lowerCAmelCase ,dynamic_thresholding_ratio=0							)
								lowerCamelCase__								=	scheduler_class(**_lowerCAmelCase							)

								lowerCamelCase__								=	10
								lowerCamelCase__								=	self.dummy_model()
								lowerCamelCase__								=	self.dummy_sample_deter.half()
								scheduler.set_timesteps(_lowerCAmelCase							)

								for i, t in enumerate(scheduler.timesteps							):
														lowerCamelCase__								=	model(_lowerCAmelCase ,_lowerCAmelCase							)
														lowerCamelCase__								=	scheduler.step(_lowerCAmelCase ,_lowerCAmelCase ,_lowerCAmelCase							).prev_sample

								assert sample.dtype == torch.floataa








"""simple docstring"""





import argparse

import torch
from torch import nn

from transformers import SpeechaTextConfig, SpeechaTextForConditionalGeneration


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[
          """encoder.version""",
          """decoder.version""",
          """model.encoder.version""",
          """model.decoder.version""",
          """decoder.output_projection.weight""",
          """_float_tensor""",
          """encoder.embed_positions._float_tensor""",
          """decoder.embed_positions._float_tensor""",
      ]
      for k in ignore_keys:
            state_dict.pop(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Dict     ):







      '''simple docstring'''




      lowerCAmelCase    		=			list(s_dict.keys()     )
      for key in keys:
            if "transformer_layers" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )
            elif "subsample" in key:
                  lowerCAmelCase    		=			s_dict.pop(SCREAMING_SNAKE_CASE     )


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any     ):







      '''simple docstring'''




      lowerCAmelCase		,						lowerCAmelCase    		=			emb.weight.shape
      lowerCAmelCase    		=			nn.Linear(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		bias=SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			emb.weight.data
      return lin_layer


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Optional[Any]				,		SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase    		=			torch.load(SCREAMING_SNAKE_CASE				,		map_location="""cpu"""     )
      lowerCAmelCase    		=			mam_aaa["""args"""]
      lowerCAmelCase    		=			mam_aaa["""model"""]
      lowerCAmelCase    		=			state_dict["""decoder.output_projection.weight"""]

      remove_ignore_keys_(SCREAMING_SNAKE_CASE     )
      rename_keys(SCREAMING_SNAKE_CASE     )

      lowerCAmelCase    		=			state_dict["""decoder.embed_tokens.weight"""].shape[0]

      lowerCAmelCase    		=			args.share_decoder_input_output_embed

      lowerCAmelCase    		=			[int(SCREAMING_SNAKE_CASE     ) for i in args.conv_kernel_sizes.split(""","""     )]
      lowerCAmelCase    		=			SpeechaTextConfig(
          vocab_size=SCREAMING_SNAKE_CASE				,		max_source_positions=args.max_source_positions				,		max_target_positions=args.max_target_positions				,		encoder_layers=args.encoder_layers				,		decoder_layers=args.decoder_layers				,		encoder_attention_heads=args.encoder_attention_heads				,		decoder_attention_heads=args.decoder_attention_heads				,		encoder_ffn_dim=args.encoder_ffn_embed_dim				,		decoder_ffn_dim=args.decoder_ffn_embed_dim				,		d_model=args.encoder_embed_dim				,		dropout=args.dropout				,		attention_dropout=args.attention_dropout				,		activation_dropout=args.activation_dropout				,		activation_function="""relu"""				,		num_conv_layers=len(SCREAMING_SNAKE_CASE     )				,		conv_channels=args.conv_channels				,		conv_kernel_sizes=SCREAMING_SNAKE_CASE				,		input_feat_per_channel=args.input_feat_per_channel				,		input_channels=args.input_channels				,		tie_word_embeddings=SCREAMING_SNAKE_CASE				,		num_beams=5				,		max_length=2_00				,		use_cache=SCREAMING_SNAKE_CASE				,		decoder_start_token_id=2				,		early_stopping=SCREAMING_SNAKE_CASE				,		)

      lowerCAmelCase    		=			SpeechaTextForConditionalGeneration(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase		,						lowerCAmelCase    		=			model.model.load_state_dict(SCREAMING_SNAKE_CASE				,		strict=SCREAMING_SNAKE_CASE     )
      if len(SCREAMING_SNAKE_CASE     ) > 0 and not set(SCREAMING_SNAKE_CASE     ) <= {
          "encoder.embed_positions.weights",
          "decoder.embed_positions.weights",
      }:
            raise ValueError(
                """Only `encoder.embed_positions.weights` and `decoder.embed_positions.weights`  are allowed to be missing,"""
                F' but all the following weights are missing {missing}'     )

      if tie_embeds:
            lowerCAmelCase    		=			make_linear_from_emb(model.model.decoder.embed_tokens     )
      else:
            lowerCAmelCase    		=			lm_head_weights

      model.save_pretrained(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("--fairseq_path", type=str, help="Path to the fairseq model (.pt) file.")
       parser.add_argument("--pytorch_dump_folder_path", default=None, type=str, help="Path to the output PyTorch model.")
       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_fairseq_sat_checkpoint_to_tfms(args.fairseq_path, args.pytorch_dump_folder_path)






